1. Technical Field
The present disclosure relates to strategies for the fundamental computer vision problem of determining shape from small (differential) motion of a camera.
2. Description of the Related Art
Computer vision is a field that includes methods for acquiring, processing, analyzing, and understanding images and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions. A theme in the development of this field has been to duplicate the abilities of human vision by electronically perceiving and understanding an image. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory. Computer vision has also been described as the enterprise of automating and integrating a wide range of processes and representations for vision perception. One fundamental computer vision problem is determining shape of an object from the small (differential) motion of a camera, for example, when the object has an unknown surface reflectance. In the general case, reflectance can be an arbitrary function of surface orientation, camera and lighting, which can be referred to as the bidirectional reflectance distribution function (BRDF). Shape and camera motion is typically solved under the umbrella of multi-view stereo methods, which rely on Lambertian assumptions, that is, assume that the image intensity does not change with camera motion. This is incorrect for objects formed of typical materials, such as metals and plastics etc.